Accurately identifying compound-protein interactions in silico can deepen our understanding of the mechanisms of drug action and significantly facilitate the drug discovery and development process. Traditional similarity-based computational models for compound-protein interaction prediction rarely exploit the latent features from current available large-scale unlabelled compound and protein data, and often limit their usage on relatively small-scale datasets. We propose a new scheme that combines feature embedding (a technique of representation learning) with deep learning for predicting compound-protein interactions. Our method automatically learns the low-dimensional implicit but expressive features for compounds and proteins from the massive amount of unlabelled data. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline for accurate compound-protein interaction prediction, even when the interaction knowledge of compounds and proteins is entirely unknown. Evaluations on current large-scale databases of the measured compound-protein affinities, such as ChEMBL and BindingDB, as well as known drug-target interactions from DrugBank have demonstrated the superior prediction performance of our method, and suggested that it can offer a useful tool for drug development and drug repositioning.
This study was conducted to examine the influences of replacing soybean meal (SBM) with fermented soybean meal (FSBM) in the diet of lactating Holstein cattle on rumen fermentation and ruminal bacterial microbiome. Twenty-four lactating Chinese Holstein dairy cattle were assigned to each of the two treatments in a completely randomized design: the SBM group [the basal total mixed ration (TMR) diet containing 5.77% SBM] and the FSBM group (the experimental TMR diet containing 5.55% FSBM). This trial lasted for 54 days (14 days for adjustment and 40 days for data and sample collection), and samples of rumen liquid were collected on 34 d and 54 d, respectively. The results showed that replacing SBM with FSBM significantly increased the molar percentages of propionate (P < 0.01) and valerate (P < 0.05), but reduced the total volatile fatty acid (TVFA) concentration (P < 0.05), butyrate molar proportion (P < 0.05), and the acetate to propionate ratio (P < 0.01). The copy numbers of total bacteria (P < 0.05), Fibrobacter succinogenes (P < 0.01), Selenomonas ruminantium (P < 0.01), and Prevotella spp. (P < 0.05) in the FSBM group were greater, while the density of Prevotella ruminicola (P < 0.05) was lower than those in the SBM treatment. Additionally, Succiniclasticum ruminis and Saccharofermentans acetigenes were significantly enriched (P < 0.05) in the rumen fluid of FSBM-fed cows, despite the fact that there was no remarkable difference in the Alpha diversity indexes, structure and KEGG pathway abundances of the bacterial community across the two treatments. It could hence be concluded that the substitution of FSBM for SBM modulated rumen fermentation and rumen bacterial microbiota in lactating Holstein dairy cows. Further research is required to elucidate the relevant mechanisms of FSBM, and provide more insights into the application of FSBM in dairy cattle.
BACKGROUNDSteam explosion is increasingly being used in the food processing industry as an efficient pretreatment technology. It is currently being used to pretreat adzuki beans at a pressure of 0.25–1.0 Mpa for 30 s and 90 s. In this study, the total polyphenol (TP) content in adzuki beans, including free polyphenols (FP) and bound polyphenols (BP), and their antioxidant activity, were determined after steam explosion treatment.RESULTSThe results showed that steam explosion can form large cavities and intercellular spaces, which aid the release of polyphenols. After steam explosion, the FP, BP, and TP content increased. The antioxidant capacity of FP and BP also increased, which demonstrated that there was a positive correlation between the polyphenol content and antioxidant capacity. Compounds of FP and BP were further identified by high‐performance liquid chromatography (HPLC). Protocatechin was the main ingredient in FP and BP, and protocatechin was higher in FP. Isoquercetin only exists in FP, and caffeic acid only in BP. After steam explosion, an increase in the protocatechin, catechin, and epicatechin content was detected in FP and BP. The phenolic compound and antioxidant capacity yield was increased at a pressure of 0.25–0.75 Mpa, however it decreased at 1.0 Mpa. A pressure of 0.75 Mpa for 90 s is the optimal condition for polyphenol separation in adzuki beans.CONCLUSIONA proper and reasonable steam explosion can effectively increase the release of phenolics and enhance the antioxidant capacity in adzuki beans. © 2020 Society of Chemical Industry
SUMMARYThe Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams, who did not know the identity or nature of the compounds, and they were asked to predict high-affinity binding among ~1,300 possible protein targets. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.